Local feature matching plays a central role in many computer vision and robotics tasks such as visual place recognition [1]}, [2]}, [3]}, image stitching [4]}, and 6-DoF pose estimation [5]}, [6]}, [7]}, [8]}.
Traditional approaches for detection and description such as SIFT [9]} and ORB [10]} first detect the points of interest and then use the patch around the pixel/keypoint to generate a descriptor. These approaches fail under severe illumination and rotation changes. Recent learning-based approaches such as SuperPoint [11]}, SuperGlue [6]}, and RoRD [7]} are trained as a `general' system for handling such challenges using training data that is augmented heavily with illumination and viewpoint variations.
